Regional flood mapping poses computational and spatial heterogeneity challenges, exacerbated by climate change-induced uncertainties. This study focuses on creating a state-wide flood mapping solution with enhanced accuracy and computational speed to support regional flooding hazard analysis and the assessment of climate change, using New Jersey as a case study. The Height Above Nearest Drainage (HAND) framework was employed for large-scale flood mapping. The model was validated against high water marks (HWMs) collected after Hurricane Irene. Based on the National Water Model (NWM), synthetic rating curves in HAND were calibrated by tuning Manning’s roughness, aligning the predicted and observed flood depths. The roughness values were generalized across the state from the validated water basins to the ungauged ones, using a multivariate regression with the hydrologic and geographic information. To map the future climate-change-induced flooding, a correlation between NOAA historical precipitation totals and NWM flow data from 2010-2020 was established to link precipitation and runoff. This study also invented a novel method for correcting catchment discontinuities, inherent in the HAND model, based on a computer vision scheme, the Sobel filter. The modeling results show that average and worst-case storm events have the potential to increase 10-50% in the state, where mountain areas and major river banks would be exposed to this impact more significantly.

Ocean waves interact with the environment in many ways. They transport energy and mass, and the resultant sea-surface roughness defines the drag coefficients that transmits wind energy to the ocean (Drennan et al., 2003). Through erosion and deposition, waves change the shape and landscape of coastal areas. Storm surge waves can cause flood damage in coastal areas. Recent studies revealed that wetlands are sensitive to the wave condition, which determines the retreat or growth of coastal ecosystems (Green and Coco, 2007; Mariotti and Fagherazzi 2010). Human activities rely on the condition of waves to conduct marine activities such as fishing, shipping, oil extraction, and offshore constructions. Thus, it is important to understand ocean waves to improve earth system modeling, protect the coastline, predict storm surge, preserve coastal ecosystems, and enhance the offshore business. This project will explore the application of synthetic aperture radar (SAR) imagery to predict significant wave height near the coast. High-frequency (HF) radar data of the ocean (aka CODAR) was used as ground truth data set to calibrate and validate the wave height estimator. Off-shore wind data was also included. The developed code will enhance the current capability to process the satellite data and create a new platform to monitor the coastal environment. The collected data will help further our understanding of the wave spectrum in a coastal environment and the data can support other research in the related topics, e.g. the interaction of waves and ice sheets, wetlands, shorelines, wind farm and aquaculture.

Recent observation reveals a stunning fact that the coastal tides are experiencing a rapid change in the last century in several places in the world. To achieve a wide and refined understanding of the phenomenon, high-accuracy tide level data is needed more than ever. In-situ measurements – the traditional and main data source to support tidal harmonic analysis – are often sparse and limited to fixed positions, insufficient to provide information about the spatiotemporal variability of tidal processes beyond the tidal gauges. Satellite altimetry may fundamentally change the situation. This technology measures water level with increased spatial coverage and resolutions. However, satellite altimetry has not been used in tidal analysis due to two major limitations in the harmonic analysis: a) a minimum length of sampled observed data is required to recognize a sufficient number of tidal constituents according to the Rayleigh criterion and b) data sampling/acquisition frequency must be at least two times the major tidal frequencies to avoid the aliasing issue dictated by Nyquist theorem. To address these issues, a novel compressed-sensing approach is proposed to break the limitations. In this method, the prior information of the regional tides (e.g., a reference tidal station near the location of interest) is collected to support a stepwise optimization process to obtain the amplitudes and phase terms of the tide signal for data series with different lengths and time intervals. Instead of least-square-fit approach, stochastic gradient decent is employed using Pytorch. A preliminary study shows that the proposed method can generate the tidal amplitudes and phases with a sampling interval of 16 days and a total data length of 30 days with an acceptable error. The results of this study can be useful to determine an optimum frequency and length for tidal data acquisition for the upcoming SWOT (Surface Water Ocean Topography) satellite, which is supposed to be launched in November 2022 to measure sea and terrestrial water level around the globe for three years and with average revisit time of 11 days.